#!/usr/bin/python3
# coding=utf-8
#
# Copyright (C) 2023-2025. Huawei Technologies Co., Ltd. All rights reserved.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# ===============================================================================

import numpy as np

import torch
# import tensorflow as tf
# bfloat16 = tf.bfloat16.as_numpy_dtype
dtype_emu = {np.float16: 1, np.float32: 2, np.int8: 3, np.int16: 4, np.int32: 5}

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def gen_golden_data_simple():


    dtype = np.float32

    ## 核间均分，单核计算量对齐:
    # input_shape = [32, 1024]

    ## 核间均分，单核计算量非对齐:
    # input_shape = [8, 1023]

    ## 核间不均分，单核计算量对齐:
    # input_shape = [32, 1023]

    ## 核间不均分，单核计算量非对齐:
    input_shape = [17, 1023]

    input_x = np.random.uniform(-10, 10, input_shape).astype(dtype)
    # golden = sigmoid(input_x).astype(dtype)
    golden = torch.sigmoid(torch.tensor(input_x)).numpy().astype(dtype)

    tiling = np.array([input_shape[0] * input_shape[1], dtype_emu[dtype]], dtype=np.uint32)

    tiling.tofile("./input/input_tiling.bin")
    input_x.tofile("./input/input_x.bin")
    golden.tofile("./output/golden.bin")


if __name__ == "__main__":
    gen_golden_data_simple()
